Downscaling of spatial irradiance based on cloud advection using transfer functions

Spatiotemporal aggregation of solar irradiance occurs when a spatially distributed receiver (e.g. a PV generation facility) collects variable geographically distributed irradiance and reduces it to a single electrical generation output. Models of this phenomenon exist, and are designed to take variability from a single point irradiance monitor and predict how that variability will be reduced by aggregation. We have applied these models in a revered manner to assess whether the models can be used to predict the variability of a single point measurement given an aggregate irradiance time series as an input. Results for an advection-based model show that this approach leads to overprediction of the high frequency variability due to overprediction of the site-to-site correlation. Incorporating predictions of site pair decorrelation from the wavelet variability model can temper the degree of overprediction and produces more realistic point time series. Further work may be warranted to further improve upon these efforts and enable reliable, transfer function-based downscaling of irradiance data

Cloud advection model of solar irradiance smoothing by spatial aggregation

Solar generation facilities are inherently spatially distributed and therefore aggregate solar irradiance in both space and time, smoothing its variability. To represent the spatiotemporal aggregation process, most existing studies focus on the reduced correlation in solar irradiance throughout a plant's spatial distribution. In this paper, we derived a cloud advection model that is instead based upon lagging correlations between upwind/downwind portions of a distributed plant, induced by advection of a fixed cloud pattern over the plant. We use the model to calculate a plant transfer function that can be used to predict the smoothing of the time series. The model was validated using the distributed HOPE-Melpitz measurement dataset, which consisted of 50 solar irradiance sensors at 1 s temporal resolution over a 3 × 2 km2 bounding area. The initial validation showed that the advection-based model outperforms other models at predicting the smoothed irradiance time series during manually identified, advection dominated conditions. We also conducted validation on the model against additional advection dominated periods in the dataset that were identified algorithmically. The cloud advection model's performance compared well to models in literature, but degraded slightly as larger cross-wind plant distributions were investigated. The results in this paper highlight the need to incorporate advection effects on spatial aggregation during advection dominated conditions. Future development of spatiotemporal aggregation models is needed to unify advective models with existing correlation reduction models and to identify regimes where each dominate

Cloud Advection and Spatial Variability of Solar Irradiance

A model for predicting smoothing of solar irradiance by spatially distributed collectors was analyzed. The model assumed cloud advection dominates the relationship between sites and represented the distributed plant with a transfer function. The transfer function representing the smoothing effect was shown to be the Fourier transform of the plant's spatial distribution, and as such, the plant represents a low-pass filter. Comparison with measured data from the HOPE-Melpitz campaign showed that the model is able to replicate dynamics present in the measured plant transfer function and showed good frequency domain agreement. Generalization of the approach is needed for broader applicability, as the current analysis only validated against one-dimensional, advection dominated conditions. However, the approach warrants further study as it has demonstrated an ability to reveal frequency domain characteristics not currently reflected by state-of-the-art models

Mesoscale model simulation of a severe summer Thunderstorm in The Netherlands: Performance and uncertainty assessment for parameterised and resolved convection

On the evening of 23 June 2016 around 18:00 UTC, a mesoscale convective system (MCS) with hail and wind gusts passed the southern province Noord-Brabant in the Netherlands, and caused 675 millions of euros damage. This study evaluates the performance of the Weather Research and Forecasting model with three cumulus parameterisation schemes (Betts-Miller-Janjic, Grell-Freitas and Kain-Fritsch) on a grid spacing of 4 km in the 'grey-zone' and with explicitly resolved convection at 2 and 4 km grid spacing. The results of the five experiments are evaluated against observations of accumulated rainfall, maximum radar reflectivity, the CAPE evolution and wind speed. The results show that the Betts-Miller-Janjic scheme is activated too early and can therefore not predict any MCS over the region of interest. The Grell-Freitas and Kain-Fritsch schemes do predict an MCS, but its intensity is underestimated. With the explicit convection, the model is able to resolve the storm, though with a delay and an overestimated intensity. We also study whether spatial uncertainty in soil moisture is scaled up differently using parameterised or explicitly resolved convection. We find that the uncertainty in soil moisture distribution results in larger uncertainty in convective activity in the runs with explicit convection and the Grell-Freitas scheme, while the Kain-Fritsch and Betts-Miller-Janjic scheme clearly present a smaller variability

Cloud advection and spatial variability of solar irradiance

A model for predicting smoothing of solar irradiance by spatially distributed collectors was analyzed. The model assumed cloud advection dominates the relationship between sites and represented the distributed plant with a transfer function. The transfer function representing the smoothing effect was shown to be the Fourier transform of the plant's spatial distribution, and as such, the plant represents a low-pass filter. Comparison with measured data from the HOPE-Melpitz campaign showed that the model is able to replicate dynamics present in the measured plant transfer function and showed good frequency domain agreement. Generalization of the approach is needed for broader applicability, as the current analysis only validated against one-dimensional, advection dominated conditions. However, the approach warrants further study as it has demonstrated an ability to reveal frequency domain characteristics not currently reflected by state-of-the-art models